



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a      :  List[str]			     =			logging.get_logger(__name__)

a      :  Optional[int]			     =			{
    'google/pix2struct-textcaps-base': (
        'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
    ),
}







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''pix2struct_text_model'''
			A      		=	['''past_key_values''']
			A      		=	{
			    '''hidden_size''': '''hidden_size''',
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}

			def __init__(self,   SCREAMING_SNAKE_CASE_=50244,   SCREAMING_SNAKE_CASE_=768,   SCREAMING_SNAKE_CASE_=64,   SCREAMING_SNAKE_CASE_=2048,   SCREAMING_SNAKE_CASE_=12,   SCREAMING_SNAKE_CASE_=12,   SCREAMING_SNAKE_CASE_=32,   SCREAMING_SNAKE_CASE_=128,   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=1E-6,   SCREAMING_SNAKE_CASE_=1.0,   SCREAMING_SNAKE_CASE_="gelu_new",   SCREAMING_SNAKE_CASE_=0,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_=0,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_=True,   **SCREAMING_SNAKE_CASE_,   )       ->   Tuple:
						UpperCAmelCase_:		List[Any]							      =     vocab_size
						UpperCAmelCase_:		List[Any]							      =     hidden_size
						UpperCAmelCase_:		List[Any]							      =     d_kv
						UpperCAmelCase_:		Optional[Any]							      =     d_ff
						UpperCAmelCase_:		Optional[int]							      =     num_layers
						UpperCAmelCase_:		List[Any]							      =     num_heads
						UpperCAmelCase_:		Optional[int]							      =     relative_attention_num_buckets
						UpperCAmelCase_:		int							      =     relative_attention_max_distance
						UpperCAmelCase_:		List[Any]							      =     dropout_rate
						UpperCAmelCase_:		Optional[Any]							      =     layer_norm_epsilon
						UpperCAmelCase_:		Dict							      =     initializer_factor
						UpperCAmelCase_:		Optional[Any]							      =     use_cache

						UpperCAmelCase_:		Dict							      =     eos_token_id
						UpperCAmelCase_:		List[Any]							      =     decoder_start_token_id

						# for backwards compatibility
						UpperCAmelCase_:		Dict							      =     dense_act_fn

						super().__init__(
						    pad_token_id=SCREAMING_SNAKE_CASE_,   eos_token_id=SCREAMING_SNAKE_CASE_,   decoder_start_token_id=SCREAMING_SNAKE_CASE_,   tie_word_embeddings=SCREAMING_SNAKE_CASE_,   is_decoder=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_,   )

			@classmethod
			def      __snake_case       (cls,   SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)       ->   "PretrainedConfig":
						cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_      ,							UpperCAmelCase_:		int							      =     cls.get_config_dict(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

						# get the text config dict if we are loading from Pix2StructConfig
						if config_dict.get("""model_type"""					) == "pix2struct":
									UpperCAmelCase_:		Optional[Any]							      =     config_dict["""text_config"""]

						if "model_type" in config_dict and hasattr(cls,   """model_type"""					) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
									    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

						return cls.from_dict(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''pix2struct_vision_model'''

			def __init__(self,   SCREAMING_SNAKE_CASE_=768,   SCREAMING_SNAKE_CASE_=768,   SCREAMING_SNAKE_CASE_=2048,   SCREAMING_SNAKE_CASE_=64,   SCREAMING_SNAKE_CASE_=12,   SCREAMING_SNAKE_CASE_=12,   SCREAMING_SNAKE_CASE_="gelu_new",   SCREAMING_SNAKE_CASE_=1E-6,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=1E-10,   SCREAMING_SNAKE_CASE_=1.0,   SCREAMING_SNAKE_CASE_=4096,   SCREAMING_SNAKE_CASE_=32,   SCREAMING_SNAKE_CASE_=128,   **SCREAMING_SNAKE_CASE_,   )       ->   Union[str, Any]:
						super().__init__(**SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Union[str, Any]							      =     hidden_size
						UpperCAmelCase_:		Any							      =     patch_embed_hidden_size
						UpperCAmelCase_:		Optional[int]							      =     d_ff
						UpperCAmelCase_:		Optional[int]							      =     dropout_rate
						UpperCAmelCase_:		Union[str, Any]							      =     num_hidden_layers
						UpperCAmelCase_:		Tuple							      =     num_attention_heads
						UpperCAmelCase_:		str							      =     initializer_range
						UpperCAmelCase_:		Optional[int]							      =     initializer_factor
						UpperCAmelCase_:		Any							      =     attention_dropout
						UpperCAmelCase_:		Tuple							      =     layer_norm_eps
						UpperCAmelCase_:		List[Any]							      =     dense_act_fn
						UpperCAmelCase_:		Tuple							      =     seq_len
						UpperCAmelCase_:		List[Any]							      =     relative_attention_num_buckets
						UpperCAmelCase_:		int							      =     relative_attention_max_distance
						UpperCAmelCase_:		Optional[int]							      =     d_kv

			@classmethod
			def      __snake_case       (cls,   SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)       ->   "PretrainedConfig":
						cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_      ,							UpperCAmelCase_:		str							      =     cls.get_config_dict(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

						# get the vision config dict if we are loading from Pix2StructConfig
						if config_dict.get("""model_type"""					) == "pix2struct":
									UpperCAmelCase_:		Tuple							      =     config_dict["""vision_config"""]

						if "model_type" in config_dict and hasattr(cls,   """model_type"""					) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
									    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

						return cls.from_dict(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''pix2struct'''
			A      		=	True

			def __init__(self,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=1.0,   SCREAMING_SNAKE_CASE_=0.0_2,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_=True,   **SCREAMING_SNAKE_CASE_,   )       ->   Optional[Any]:
						super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE_,   is_encoder_decoder=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

						if text_config is None:
									UpperCAmelCase_:		int							      =     {}
									logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""					)

						if vision_config is None:
									UpperCAmelCase_:		List[str]							      =     {}
									logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""					)

						UpperCAmelCase_:		Optional[Any]							      =     PixaStructTextConfig(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     PixaStructVisionConfig(**SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Any							      =     self.text_config.decoder_start_token_id
						UpperCAmelCase_:		Tuple							      =     self.text_config.pad_token_id
						UpperCAmelCase_:		str							      =     self.text_config.eos_token_id

						UpperCAmelCase_:		str							      =     initializer_factor
						UpperCAmelCase_:		List[str]							      =     initializer_range

						UpperCAmelCase_:		Dict							      =     self.initializer_range
						UpperCAmelCase_:		Dict							      =     self.initializer_range

						UpperCAmelCase_:		Optional[int]							      =     is_vqa

			@classmethod
			def      __snake_case       (cls,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)       ->   Tuple:
						return cls(text_config=text_config.to_dict(),   vision_config=vision_config.to_dict(),   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		Union[str, Any]							      =     copy.deepcopy(self.__dict__					)
						UpperCAmelCase_:		List[str]							      =     self.text_config.to_dict()
						UpperCAmelCase_:		Optional[Any]							      =     self.vision_config.to_dict()
						UpperCAmelCase_:		Optional[int]							      =     self.__class__.model_type
						return output











def  lowerCAmelCase_	(lowerCAmelCase__:		int  ):





			"""simple docstring"""


			if num <= 0:
						raise ValueError("""Input must be a positive integer"""  )

			UpperCAmelCase_:		Tuple							      =     [True] * (num + 1)

			UpperCAmelCase_:		Tuple							      =     2
			while p * p <= num:
						if primes[p]:
									for i in range(p * p		,						num + 1		,						lowerCAmelCase__  ):
												UpperCAmelCase_:		Optional[Any]							      =     False
						p += 1

			return [prime for prime in range(2		,						num + 1  ) if primes[prime]]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	a      :  Optional[Any]			     =			int(input('Enter a positive integer: ').strip())
	print(prime_sieve_eratosthenes(user_num))






